Computer processes for predicting media item popularity

ABSTRACT

Systems and methods are disclosed that identify users of a media distribution system that tend to consume popular media items prior to such media items gaining popularity. For example, a set of early adopters may be identified that tend to listen to music associated with particular artists before such artists become popular. The systems and methods disclosed may also utilize identified early adopters to determine relatively obscure or unpopular media items (or creators thereof) that are likely to become popular in the future. Illustratively, an obscure artist whose content is commonly consumed by early adopters can be identified as potentially achieving widespread popularity in the future. These media items predicted to become popular or media item creators may then be recommended to other users of the media distribution system.

BACKGROUND

Digital encoding has rapidly expanded the influence of consumable mediasuch as music, books, and video, by decreasing development andproduction costs while increasing accessibility of end users. Forexample, global computer networks, such as the internet, allow mediacreators to collaborate and publish media content without reliance ontraditional distribution channels. These networks also allow users toeasily locate and consume desired media at their convenience. Forexample, network-based services now exist that enable users to digitallystream or download music, books, and video for consumption on the user'scomputing device.

Sales, consumption, and popularity of media items often experience alarge amount of inequality, with a small number of media items beingvery prominent and a larger number of media items being relativelyobscure. In addition, the popularity of a given media item may beself-influencing. For example, users may be exposed to a popular songfrequently (e.g., via radio, TV, movies, etc.), thereby increasing thesong's prominence. Meanwhile, users may rarely be exposed to an unknownsong, ensuring the song remains obscure. Given the extensive selectionof songs available, it may be difficult or impossible for users tolocate obscure media content without knowing the specific details of themedia content. Such difficulty negatively affects users, mediaproducers, and media content providers by limiting users' ability toacquire, purchase or consume desirable media content.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

FIG. 1 schematically illustrates an embodiment of an electronic catalogsystem that determines early adopters of popular media items based onthe acquisition and consumption history of the early adopters. Theelectronic catalog system can further determine media items that arelikely to become popular in the future based on habits of the earlyadopters. Moreover, the electronic catalog system can providerecommendations to users based on media items that are predicted tobecome popular in the future.

FIG. 2 illustrates an embodiment of a process for determining earlyadopters of popular media items based on popularity histories of suchmedia items and based on acquisition and consumption histories of theearly adopters.

FIG. 3 illustrates an embodiment of a process for determining one ormore obscure media items that are commonly acquired or consumed by earlyadopters, and that therefore may be likely to become popular in thefuture.

FIG. 4 illustrates an example format of a web page that may be generatedby the electronic catalog system to enable users to browse for andconsume media items. The web page of FIG. 4 may further provide userswith recommendations for additional media items based on media itemsthat have been commonly acquired or consumed by early adopters.

DETAILED DESCRIPTION

Aspects of the present disclosure generally relate to systems andcomputerized processes for identifying users that tend to repeatedlyacquire or consume media items, such as media items created by aspecific author or artist, prior to such media items (or the creators ofsuch media items) becoming widely recognized or popular. Illustratively,media items may include various types of works such as songs, movies,books, videos, or any other consumable media content. Creators of mediaitems may include artists, producers, authors, editors, engineers,technicians, or other parties associated with creation, production, ordistribution of media. Generally, users who tend to consume media itemsprior to such items becoming popular may be referred to herein as “earlyadopters.” As will be described in more detail below, interactioninformation regarding a large number of users (e.g., all users of anetwork-based media distribution system) may be analyzed to determine asubset of those users that consistently interact with media items priorto those items becoming popular. Further, aspects of the presentdisclosure relate to computer-implemented algorithms that utilizeacquisition and consumption histories of these early adopters toidentify relatively unknown or obscure media items or media itemcreators that have a high potential for becoming popular in the future.Specifically, as will be described in more detail below,computer-implemented algorithms may be used to gather a high volume ofearly adopter interaction data and to process this data to determinecorrelations between the interactions of multiple early adopters. Thesecorrelations may then be ranked according to a scoring algorithm topredict obscure media items that are likely to become popular in thefuture. Still further, aspects of the present disclosure relate tosurfacing these identified media items to users of acomputer-implemented content service, such as a network-based mediadistribution system. Illustrative, media items may be surfaced to usersby providing recommendations of media items predicted to become popularor media item creators to users of the computer-implemented contentservice.

Specifically, a computer-implemented media content service such as anetwork-based streaming music service, is disclosed that identifiesthose users that tend to acquire or consume media items (such as mediaitems associated with a particular media item creator) prior to thosemedia items becoming widely popular (e.g., “early adopters” of the mediaitems). In one embodiment, the media content service identifies earlyadopters based on acquisition and consumption data of current or pastpopular media items. For example, the media content service mayaggregate information regarding users that consumed a media item priorto the media item becoming popular. Thereafter, the media contentservice may score each user to determine the likelihood that the user isan early adopter. Illustratively, a user who consumed one media itemprior to that item becoming popular may be less likely to represent anearly adopter than a user who has consumed many media items prior tothose items becoming popular. Various additional metrics for scoringusers will be discussed below, including metrics based on the timeperiod during which a user consumed or acquired a now-popular media itemand metrics based on the historical level of interest of a user in a nowpopular media item.

After identifying a number of early adopters, the media content serviceis enabled to determine commonalities among those early adopters, suchas obscure media items that tend to be consumed by the early adopters(e.g., songs by a particular artist). Because early adopters have beenidentified based on their history of early interaction with media itemsthat later become popular, a relatively large number of early adoptersconsuming obscure media items by a single artist may be indicative offuture popularity of the artist.

In one embodiment, the media content service may score artists whosesongs are consumed or acquired by early adopters based, for example, onthe number of early adopters that have acquired or consumed the artist'ssongs and/or on the interest level of those early adopters in theartist's songs. Thereafter, the media content service can utilize thedetermined song scores to provide recommendations to users regardingartists predicted to become popular. For example, the media contentservice may provide recommendations to users of “up and coming” artistsidentified based on the determined artist scores. Such recommendationscan be provided, for example, via applications executing on usercomputing devices (e.g., web browsers or mobile applications),electronic message (e.g., electronic mail, short message service,instant message) or other transmissions. By interacting with suchrecommendations, users are enabled to acquire and/or consume songs bythe recommended artist. As a further example, the media content servicecan provide a “playlist” including songs by a number of identifiedartists, and enable users to consume each song within the playlist.

Accordingly, the systems and methods provided herein enable a contentservice or other entity to identify unknown or obscure media items(e.g., songs, artists, etc.) that are likely to become popular in thefuture. The systems and methods provided herein further enable a contentservice or other entity to surface to users media items predicted tobecome popular, thereby increasing the diversity of media consumed byusers, increasing user satisfaction with the media content service, andincreasing revenue to both the content service and media creators.

While embodiments of the present disclosure are generally described withreference to musical media items or creators thereof, the systems andmethods provided herein can be utilized with respect to any consumablemedia, including books, articles, movies, and videos. Aspects of thepresent disclosure may be particularly suited to environments where alarge inequality in popularity between media items exists, such asamateur and user-created video submission sites. For example, aspects ofthe present disclosure can be utilized to determine a video which islikely to “go viral” (e.g., gain a mass popularity) in the future.

Further, while embodiments of the present disclosure are generallydescribed with reference to individual media items or creators thereof,the systems and methods provided herein can also be utilized withrespect to groupings of media items or media item creators, such asalbums, playlists, bands, collaborative groups of artists, recordlabels, etc.

Still further, while embodiments of the present disclosure are generallydescribed with respect to a media distribution service, the systems andmethods provided herein may be utilized by any system or service capableof collecting interaction information from a user base. For example, insome instances, embodiments of the present disclosure may include or beutilized by a media information system configured to interact withstand-alone applications on a number of user computing devices.Illustratively, such stand-alone applications may include media playeror manager applications on the user device, and such a media informationsystem may be a network-based service which interacts with thestand-alone applications to provide information regarding media items.

FIG. 1 schematically illustrates one embodiment of an electronic catalogsystem 110 that implements the above and other features. The electroniccatalog system 110 may be implemented as a computerized system thatcomprises multiple programmed computing devices (e.g., web servermachines, application servers, storage servers, load balancers, etc.)that communicate over one or more networks. The electronic catalogsystem 110 hosts a web site or other network-based interactive servicethat provides functionality for users to browse an electronic catalog ofmedia items, such as music items, that are available for consumptionand/or acquisition. Illustratively, users of such a web site may beenabled to download media items for later consumption, to consume mediaitems directly from the electronic catalog system 110 (e.g., bystreaming the media item), or both. Although described in the context ofa web site, the inventive features described herein can also beimplemented in other types of interactive systems, including other typesof network-based services that users access from user computing devicessuch as mobile phones, personal digital media players, in-carentertainment systems, televisions, set top boxes, audio visualreceivers, etc.

As illustrated in FIG. 1, the electronic catalog system 110 includes oneor more web servers 112 that respond to page requests received over thenetwork 104 from user computing devices 102 (e.g., personal computers,portable computing devices, mobile phones, electronic book readers,PDAs, in-car entertainment systems, televisions, set top boxes, audiovisual receivers, etc.). Those skilled in the art will appreciate thatthe network 104 may be any wired network, wireless network orcombination thereof. In addition, the network 104 may be a personal areanetwork, local area network, wide area network, cable network, satellitenetwork, cellular telephone network, or combination thereof. In theillustrated embodiment, the network 104 is the Internet. Protocols andcomponents for communicating via the Internet or any of the otheraforementioned types of communication networks are well known to thoseskilled in the art of computer communications and thus, need not bedescribed in more detail herein.

The catalog system 110 also includes a repository of catalog content120. The catalog content may include, for example, media itemsthemselves (e.g., music, videos, movies, audio books, electronic books,etc.), related images, product descriptions, user ratings and reviews ofparticular media items, price and availability data, etc. A searchengine (not shown) enables users to search the catalog by submittingfree-form search strings.

The catalog system 110 also includes a repository 118 of user accountdata for users who have created accounts with the system (“users”). Useraccount data may include, for example, usernames, passwords, paymentinformation, shipping information, item ratings, and wish lists. Therepository 118 may also include various types of collected behavioraldata reflective of the user's activity. For example, the behavioral datamay include purchase or acquisition histories, search histories,consumption histories, comments regarding media items, reviews of mediaitems and ratings of media items.

As shown in FIG. 1, the electronic catalog system 110 also includes apopularity prediction service 114 that predicts media items or creatorsthereof that are predicted to become popular based on user history ofearly adopters. Specifically, the popularity prediction service 114inspects the consumption, acquisition, and interaction history of usersidentified as early adopters to locate obscure media items (or creatorsof obscure media items) common to multiple early adopters. As will bediscussed in more detail with respect to FIG. 3, below, the popularityprediction service 114 further prioritizes or scores media items (and/orcreators thereof) common to multiple early adopters to determine alikelihood of future popularity. The popularity prediction service 114then makes information regarding media items and/or media item creatorspredicted to become popular available to users (e.g., in conjunctionwith the web server 112) and other components of the electronic catalogsystem 110.

In one instance, information regarding media items predicted to becomepopular can be made available to users via a display page provided bythe web server 112. Such pages are illustratively displayed by a webbrowser (or other user interface) when a user computing device 102visits the web site hosted by the electronic catalog system 110. Thesepages may enable a user to interact with various aspects of theelectronic catalog system 110, such as by browsing for and acquiringmedia items, receiving information regarding media items, consumingmedia items directly from the electronic catalog system 110 (e.g., bystreaming such media items), downloading media items to the usercomputing device 102, otherwise interacting with media items (e.g., bycommenting on, reviewing, or rating a media item), and receivingrecommendations regarding media items that may be of interest to a user(e.g., including media items or creators identified as potentiallybecoming popular in the future). One example of a detail page includinga recommendation for a media item identified as potentially becomingpopular in the future will be discussed with reference to FIG. 4, below.

The popularity prediction service 114 includes, and operates inconjunction with, an early adopter identification engine 116. The earlyadopter identification engine 116 is configured to identify, based onthe interactions of users of the electronic catalog system 110, a subsetof users that tend to consume popular media items before they becomepopular. These users are generally referred to herein as “earlyadopters.” The early adopter identification engine 116 can identify suchearly adopters at least in part based on an analysis of current and pastpopular media items. For example, the early adopter identificationengine 116 may aggregate user data for all or a threshold number ofpopular songs, and determine those users that acquired, consumed orinteracted with the song prior to the song becoming popular. Usersrepresented by this user data may then be ranked or scored according toa variety of metrics, including but not limited to the popularity of therelevant song, the time period during which the user acquired, consumedor interacted with the song, and the frequency of that consumption orinteraction. As will be described in more detail below, these metricsmay generally be assessed relative to the point at which a media itemgained popularity, rather than relative to an initial offering of themedia item or a release date of the media item. After scoring theidentified users, the early adopter identification engine 116 maydetermine a set of such users that represent early adopters based on theuser scores. One illustrative routine for identifying early adoptersbased on user data will be described below in more detail with respectto FIG. 2.

In some instances, the electronic catalog system 110 incentivizes orrewards identified early adopters. Illustratively, users identified asearly adopters can be awarded special status within the electroniccatalog system 110, such as a special title, designation or “badge”associated with the user. Such status may be viewable to the user and/orother users of the electronic catalog system 110 (e.g., with consent ofthe early adopter). In some such instances, other users of theelectronic catalog system 110 may be enabled to view or track theconsumption habits of early adopters. This may enable users to “follow”early adopters that tend to consume desirable music. Further, earlyadopters may be given financially or tangentially valuable rewards, suchas gift certificates or credit usable on the electronic catalog system110 for purchase of media content or other items. Provision of rewardsto early adopters may incentivize users to seek out currently unpopularmusic, thereby increasing the user's exposure to the content availableon the electronic catalog system 110.

In some embodiments, the system 110 can be configured differently thanshown in FIG. 1. For example, the popularity prediction service 114 maybe operated independently from the electronic catalog system 110 (e.g.,on its own or associated with additional network-based services), or maybe implemented without any connection to an electronic catalog system.Many variations and alternatives are possible, and no single componentor group of components is essential or required to be present in everyembodiment of the system 110.

The electronic catalog system 110 may also include functionality andcomponents (not shown) for enabling users to perform various other typesof functions, including but not limited to the following: (1) purchasingor acquiring media items selected from the electronic catalog, (2)creating wish lists of media items selected from the catalog, andsearching for and viewing the wish lists of other users, (3) conductingkeyword searches for specific media items, (4) browsing the catalogusing a category-based browse tree, (5) creating personal profiles thatare viewable by other users, (6) tagging specific catalog items, and (7)posting user reviews, reviews, and ratings of particular media items. Insome embodiments, the electronic catalog system 110 may be associatedwith or combined with other catalog systems, such as systems forpurchasing physical goods from one or more merchants. Still further, insome embodiments the electronic catalog system 110 may include one ormore components configured to interact with other external systems, suchas social networking systems. Illustratively, the electronic catalogsystem 110 may interact with such social networking systems in order togather additional data regarding the popularity of media items and/orthe interactions of early adopters. Illustrative systems and methods forinteracting with a social networking system are described in more detailwithin U.S. Pat. No. 8,355,955 issued to Mirchandani et al., entitled“Method, Medium, and System for Adjusting Selectable Element Based onSocial Networking Usage,” which is hereby incorporated in its entirety.

Any one or more of the web server 112, the page template repository 112,the popularity prediction service 114, the early adopter identificationengine 116, the user account and behavior data repository 118, and thecatalog content 120 may be embodied in a plurality of components, eachexecuting an instance of the respective page template repository 112,popularity prediction service 114, early adopter identification engine116, user account and behavior data repository 118, and catalog content120. A server or other computing component implementing any one of thepage template repository 112, the popularity prediction service 114, theearly adopter identification engine 116, the user account and behaviordata repository 118, and the catalog content 120 may include a networkinterfaces, memories, processing units, and computer readable mediumdrives, all of which may communicate which each other may way of acommunication bus. The network interfaces may provide connectivity overthe network 104 and/or other networks or computer systems. Theprocessing units may communicate to and from memory containing programinstructions that the processing unit executes in order to operate therespective page template repository 112, popularity prediction service114, early adopter identification engine 116, user account and behaviordata repository 118, and catalog content 120. The processing units andmemories may be selected in accordance with the processing requirementsof embodiments of the present application. For example, where theelectronic catalog system 110 is an international music distributionservice including interaction information from millions of users,multiple processors and memories (e.g., from multiple computing devicesworking in conjunction) may be required to implement the functionalitiesof, e.g., the popularity prediction service 114). Memories may generallyinclude RAM, ROM, other persistent and auxiliary memory, and/or anynon-transitory computer-readable media.

FIGS. 2 and 3 describe example routines for identifying creators ofmedia items, such as musical artists, that are predicted to becomepopular in the future based on the interactions of early adopters.Specifically, FIG. 2 describes an illustrative routine 200 carried outby the early adopter identification engine 116 for identifying earlyadopters based on current or historically popular artists. FIG. 3describes an illustrative routine 300 carried out by the popularityprediction service 114 for identifying artists that will potentially bepopular in the future based on common acquisition and/or consumption ofthe artist's works by a number of early adopters. Though theillustrative routines of FIGS. 2 and 3 are described with respect to amusical artist, in some embodiments these routines may be utilized toidentify creators of other media items, such as movies, books, orvideos, that are predicted to become popular, or to identify media itemsthemselves that are predicted to become popular.

With reference to FIG. 2, the illustrative routine 200 for identifyingearly adopters begins at block 202, where the early adopteridentification engine 116 identifies a number of current or historicalpopular artists. Popular artists can be identified based on informationassociated with the electronic catalog system 110, on externallysupplied information (e.g. information from a social networking system),or a combination thereof. For example, popular artists may be identifiedbased on artists whose songs have been most consumed or purchased on theelectronic catalog system 110 during a given period of time (e.g., day,week, month, year, etc.), artists with the highest revenue during such atime period, artists associated with the most incoming search queriesduring a time period, etc. As a further example, popular artists can beidentified based at least in part on external data, including sales data(e.g., as may be provided by the Recording Industry Association ofAmerica (RIAA)) or popularity data (e.g., as may be provided byBillboard magazine or other publications in the form of popularitycharts). As noted above, in some instances the electronic catalog system110 may be configured to interact with other, external systems, such asa social networking system. In such instances, popularity data mayfurther be based on user interactions on the social networking system,such as posts regarding an artist created by users of the socialnetworking system, users “following” an artist on the social networksystem, or endorsements of an artist by users of the social networkingsystem (e.g., by indication that a user “likes” an artist).

As still a further example, popular artists may be identified based ontheir relative popularity over a period of time. For example, an artiststhat experiences a relative drop in popularity over a given period oftime may be less likely to become popular than an artist thatexperiences relative rise in popularity. In some instances, an artist'srelative popularity may be referred to as a “popularity velocity.”Similarly, in some instances popular artists may be identified based ontheir relative popularity velocity over a given period of time. Forexample, an artist with a large increase in popularity velocity may bemore likely to be identified as popular. Relative popularity velocityover time may also be referred to as “popularity acceleration.”

In some instances, threshold values may be applied to determine whetheran artist is sufficiently popular. For example, artists may be deemedpopular only if data associated with the artist satisfies an absolutethreshold, such as volume of sales or consumption by users of theelectronic catalog system 110. As a further example, artists may bedeemed popular only if data associated with the artist satisfies arelative threshold, such as falling within the top number or percentageof sales with respect to other artists. One skilled in the art willappreciate that a combination of criteria may be used to identify adesired quantity of popular artists.

At block 204, for each popular artist previously identified, the earlyadopter identification engine 116 determines a number of users of theelectronic catalog system 110 that acquired or consumed songs of thepopular artist prior to the artist becoming popular. In someembodiments, to facilitate identification of these users, the earlyadopter identification engine 116 may identify a “breakout point” of theartist (e.g., a point at which the artist become sufficiently popular).This “breakout point” may be determined based on the same or similarcriteria described above with respect to determining the popularity ofan artist. For example, the early adopter identification engine 116 maydetermine a historical point at which the artist no longer satisfies aset of criteria for establishing a popular artist. In one example, theset of criteria for establishing a popular artist (e.g., as describedabove with reference to block 202) is the same as the criteria used todetermine a breakout point. In another example, the criteria forestablishing a popular artist may be different than the criteria used toestablish a breakout point, e.g., such that the breakout point occursearlier than the point at which the artist could be deemed popular.

After determining such a breakout point, the early adopteridentification engine 116 can identify users of the electronic catalogsystem 110 that interacted with the given popular artist prior to thebreakout point. Interactions with an artist may include, by way ofnon-limiting example, listening to songs by the artist, purchasing songsby the artist, searching for information regarding the artist on theelectronic catalog system 110, commenting on the artist within theelectronic catalog system 110, or otherwise displaying an interest inthe artist on the electronic catalog system 110. Further, in someembodiments, interactions with the artist may occur on external systems.For example, where the electronic catalog system 110 is configured tointeract with external social networking systems, interactions on thesesocial networking systems can be utilized to establish a user as anearly adopter.

At block 206, the early adopter identification engine 116 scores eachuser identified at block 204 to determine the extent to which each useris an “early adopter” of artists. Scoring is based, for example, on oneor more metrics including but not limited to the frequency ofconsumption of the artist's songs by the user, number of purchases ofthe artist's work by the user, number of other interactions (e.g.,comments, ratings, reviews, or searches) with the artist by the user. Inone instance, each metric is weighted according to timing of theassociated action by the user. For example, interaction with an artistby a user that occurs well prior to the breakout point of the artist maybe weighted more highly interactions with the artist that occur near tothe breakout point of the artist. In one instance, such weighting isproportional to the length of time between the interaction and thebreakout point of the artist. In still more instances, scores of usersmay be based on additional criteria, such as the location of the user ordemographics of the user (e.g., age, gender, etc.). Illustratively,users accessing the electronic catalog system 110 from specificgeographic areas (e.g., areas that commonly identify popular artistsprior to the artist becoming popular) may be scored more highly thanusers accessing the electronic catalog system 110 from other geographicareas.

After determining a score of each user, the early adopter identificationengine 116 utilizes the determined scores to rank the users and identifya set of the users as “early adopters.” In one instance, the earlyadopter identification engine 116 can identify early adopters based onan absolute score threshold, such that any user with a score satisfyingthe threshold is designated as an early adopter. In another embodiment,the early adopter identification engine 116 can identify early adoptersbased on a relative threshold, such that the top X number or Xpercentage of users scored are designated as early adopters. Acombination of absolute and relative thresholds may also be utilized.Thereafter, the routine 200 may end at block 210. As described below,the early adopters identified by the routine 200 can thereafter beutilized to identify currently obscure artists that are likely to obtainpopularity in the future.

Though the routine 200 is described with reference to all artistsrepresented within the electronic catalog system 110, the routine 200may alternatively be implemented to determine early adopters withrespect to only a subset of artists. For example, the routine 200 may beexecuted with respect to artists within a specific genre to identifyearly adopters of the specific genre. Similarly, the routine 200 may beexecuted with respect to a specific geographic region, either todetermine early adopters of artists associated with a specific region,to determine early adopters who are themselves within a specific region,or both. Accordingly, in some instances, the early adopteridentification engine 116 can implement the routine 200 multiple timesto determine early adopters associated with differing characteristics.

One skilled in the art will appreciate that routine 200 may includeadditional or alternative components. For example, as described above,in some instances early adopters may be granted rewards (e.g., afinancial reward, a badge or other indicator, etc.). Accordingly, inthese instances the routine 200 may include additional components (notshown in FIG. 2) to distribute such rewards to identified earlyadopters).

With reference to FIG. 3, the illustrative routine 300 for identifyingartists predicted to become popular begins at block 302, where thepopularity prediction service 114 identifies artists whose songs havebeen consumed or otherwise interacted with (e.g., by acquisition,commenting, searching, etc.) by early adopters (e.g., as identified bythe routine 200 of FIG. 2). In one embodiment, block 302 includescollecting all interaction data of early adopters, and identifying eachartist with which an early adopter interacted. In another embodiment,block 302 includes identifying only a subset of artists interacted withby early adopters, such as artists meeting a threshold interactionlevel. In this manner, artists associated with very low interactionrates (e.g., interaction by only a single early adopter) may be removedfrom further consideration by the routine 300.

Thereafter, at block 304, the popularity prediction service 114 filtersalready popular artists from further processing, in order to avoididentifying already popular artists as potentially becoming popular inthe future. Metrics for identifying popularity of an artist aredescribed in more detail above with respect to FIG. 2, but may includeutilizing popularity information gathered within the electronic catalogsystem 110 (e.g., purchases, song consumption, ratings, etc.) as well asexternal information (e.g., record charts, record sales, etc.).

At block 306, the remaining identified artists may be scored based onthe level of interaction of early adopters. Illustratively, scoring isbased on a number of interactions with an identified artist across allearly adopters. Accordingly, in one embodiment, the score of an artistis directly proportional to the aggregate number of interactions withthe artists (or works of the artist) across all early adopters. As notedabove, interactions may include playing a song of the artist, searchingfor an artist or song of the artist, commenting on, reviewing, or ratingan artist, or otherwise indicating an interest in the art to theelectronic catalog system 110. Interactions may occur on the electroniccatalog system 110 itself, or via external systems (e.g., socialnetworking systems).

In some instances, interactions of early adopters may be weighted, suchthat some interactions affect the score of an artist more heavily thanothers. Illustratively, commenting on an artist may be weighted moreheavily than searching for an artist, while purchasing an artist's worksmay be weighted more heavily than consuming the artist's works. In stillfurther embodiments, interactions of specific early adopters may beweighted based on characteristics of the early adopter. Illustratively,each interaction by an early adopter may be weighted based on a score orranking of the early adopter (e.g., as determined in blocks 206 and 208of FIG. 2, described above). The score of an identified artist may bebased on any combination of the above, exclusively or in combinationwith other metrics.

Thereafter, at block 308, the popularity prediction service 114 utilizesthe score of each artist to identify artists that are likely to becomepopular in the future. In one instance, the popularity predictionservice 114 can predict the future popularity of artists based on anabsolute score threshold, such that any artist with a score satisfyingthe threshold is predicted to become popular in the future. In anotherembodiment, the popularity prediction service 114 can predict the futurepopularity of artists based on a relative threshold, such that the top Xnumber or X percentage of artists scored are predicted to become popularin the future. A combination of absolute and relative thresholds mayalso be utilized. Thereafter, the routine 300 may end at block 310. Aswill be described below, the artists predicted by the routine 300 topotentially become popular can thereafter be utilized to providerecommendations regarding the artists or artists' songs to users of theelectronic catalog system 110.

FIG. 4 illustrates an example of the format and content of arepresentative display page 400 (e.g., web page) that is generated bythe electronic catalog system 110 and enables users to browse andconsume media content, as well as receive recommendations regardingmedia items predicted to become popular in the future. Therepresentative display page 400 is presented as it may appear, forexample, in a web browser. Illustratively, the display page 400 isgenerated by the web server 112 of FIG. 1 using a repository of pagetemplates 114. As shown in FIG. 4, the display page 400 providesinformation retrieved from the electronic catalog system 110, i.e., the“Content Catalog” 402 to “Yvette User” 404, an illustrative useraccessing the “Content Catalog.” The display page 400 further includes aset of navigation links 405 enabling Yvette User to navigate to otherdisplay pages of the Content Catalog, including display pages forediting user settings, exiting the Content Catalog, shopping for mediacontent, etc., as well as a search input 406 enabling Yvette User tosearch for media items on the Content Catalog.

As shown in FIG. 4, the display page 400 enables Yvette User to interactwith various media items available on the Content Catalog. While thedisplay page 400 is described herein with reference to music and musicalartists, the same or similar display pages may be used to displayinformation regarding other forms of media items, such as books orvideos. The display page 400 also includes a browsing portion 408, whichenables Yvette User to select different modes of interacting with thedisplay page 400, as well as a media information portion 410 displayinginformation regarding one or more media items. Specifically, thebrowsing portion 408 includes one or more links (e.g., “Songs,”“Albums,” “Artists”) selectable by Yvette User to modify informationshown within the media information portion 410. For example, selectionof the “Songs” link within the browsing portion 408 modifies the mediainformation portion 410 to reflect information regarding all or a subsetof songs acquired, purchased, or accessible to Yvette User on theContent Catalog. Similarly, selection of the “Album,” “Artist” or“Genre” links within the browsing portion 408 modifies the mediainformation portion 410 to reflect information regarding all or a subsetof media items corresponding to respective albums, artists, or genresacquired, purchased, or accessible to Yvette User on the ContentCatalog. In the illustrative display page 400, the media informationportion 410 reflects information regarding albums and songs accessibleto Yvette User within the “Pop” genre. By interaction with the mediainformation portion 410, Yvette User may select individual songs oralbums for playback (e.g., directly from the Content Catalog) ordownload (e.g., for later consumption).

The display page 400 further includes a status bar 412 enabling YvetteUser to consume media items, such as the songs reflected in the mediainformation portion 410. Yvette User may select various portions of thestatus bar to begin, pause, or halt playback, to skip to other mediaitems, or to change playback position within a current media item. Asshown in FIG. 4, the status bar 412 further includes informationregarding a currently played media item, such as the current playbackposition relative to the duration of the media item.

Still further, the display page 400 includes a recommendation portion414 depicting information regarding one or more media items predicted tobecome popular by the Content Catalog (e.g., by implementation of theroutine 300 of FIG. 3). Specifically, the recommendation portion 414 ofFIG. 4 includes information regarding two obscure songs 416 and 418 thathave been frequently interacted with by users identified as earlyadopters within the “pop” genre. Accordingly, each of these songs iscurrently associated with an artist who has not become popular in the“pop” genre, but who is predicted to potentially become popular in thefuture. The artists of these songs are therefore identified as “up andcoming” artists the Content Catalog. By selection of the informationregarding the songs 416 and 418, Yvette User can request playback of therespective songs 416 and 418 on the Content Catalog, thereby gainingexposure to the identified artists.

The recommendation portion 414 also includes additional informationregarding the recommended songs that may be of interest to Yvette User.For example, information portions 418 and 422 reflect the number ofearly adopters that have listened to the artist associated with therespective songs 416 and 418. In addition, the recommendation portion414 includes controls that enable Yvette User to further interact withmedia items by “up and coming” artists, such as link 422.Illustratively, selection of link 422 enable Yvette User to listen to aplaylist populated with media items generated by artists that arepredicted to become popular, and may display information regarding sucha playlist within the media information portion 410. Accordingly, byinteraction with the recommendation portion 414, Yvette User is enabledto view information regarding artists predicted to become popular in thefuture. The information may benefit Yvette User, by increasing the easeof discovering new artists and media, as well as the artist, byincreasing exposure and consumption of their works.

Each of the processes, methods, and algorithms described in thepreceding sections may be automated by a computer system that includesone or more computing devices, each of which includes a memory and aprocessor that includes digital logic circuitry. For example, theprocesses may be embodied in, and fully or partially automated by, codemodules executed by one or more computers, computer processors, ormachines configured to execute computer instructions. The code modulesmay be stored on any type of non-transitory computer-readable medium orcomputer storage device, such as hard drives, solid state memory,optical discs, and/or the like. The systems and modules may also betransmitted as generated data signals (e.g., as part of a carrier waveor other analog or digital propagated signal) on a variety ofcomputer-readable transmission mediums, including wireless-based andwired/cable-based mediums, and may take a variety of forms (e.g., aspart of a single or multiplexed analog signal, or as multiple discretedigital packets or frames). The processes and algorithms may also beimplemented partially or wholly in application-specific circuitry. Theresults of the disclosed processes and process steps may be stored,persistently or otherwise, in any type of non-transitory computerstorage such as, e.g., volatile or non-volatile storage. In otherembodiments, the results of the disclosed process and process step maybe stored in transitory computer storage, such as a signal. Thepopularity prediction service 114 may, for example, be implemented by aphysical server that comprises one or more computing devices.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

While certain example embodiments have been described, these embodimentshave been presented by way of example only, and are not intended tolimit the scope of the inventions disclosed herein. Thus, nothing in theforegoing description is intended to imply that any particular feature,characteristic, step, module, or block is necessary or indispensable.Indeed, the novel methods and systems described herein may be embodiedin a variety of other forms; furthermore, various omissions,substitutions and changes in the form of the methods and systemsdescribed herein may be made without departing from the spirit of theinventions disclosed herein. The accompanying claims and theirequivalents are intended to cover such forms or modifications as wouldfall within the scope and spirit of certain of the inventions disclosedherein.

What is claimed is:
 1. A computer-implemented method of identifyingmusical artists that are likely to become popular, thecomputer-implemented method comprising: determining a first set ofmusical artists have achieved a selected popularity level on anetwork-based music distribution system based at least in part on one ormore popularity metrics; retrieving artist interaction informationreflecting user interaction with the music distribution system regardingthe first set of musical artists; determining a set of users of themusic distribution system that interacted with content related toindividual artists of the first set of musical artists prior topopularity of the individual artists, wherein the set of users aredetermined based at least in part on the artist interaction information;assigning scores to individual users of the set of users, wherein ascore of an individual user is based at least in part on a length oftime between i) an initial interaction of the individual user withcontent related to the individual artists and ii) a point in time atwhich the individual artists achieved the selected popularity level;retrieving early adopter interaction information reflecting interactionsof the determined set of users with content of a second set of artistsnot yet popular on the music distribution system; ranking the second setof artists based at least in part on the early adopter interactioninformation and the scores of individual users corresponding to theearly adopter interaction information; selecting at least one artist ofthe second set of artists based at least in part on the ranking to forma recommendation; and transmitting the recommendation of the selected atleast one artist to at least one user of the music distribution system;wherein said method is performed entirely by a computer system thatcomprises one or more computing devices.
 2. The computer-implementedmethod of claim 1, wherein the one or more popularity metrics compriseat least one of a level of interaction with content of an artist on thenetwork-based music distribution system, a level of interaction with theartist on an external system, an externally published popularity rankingof the artist, or reported sales of the artist by an externalorganization.
 3. The computer-implemented method of claim 2, wherein thelevel of interaction with the artist on the network-based musicdistribution system includes at least one of a number of times users ofthe network-based music distribution system have consumed media contentassociated with the artist, viewed information regarding the artist ormedia content associated with the artist, searched for the artist ormedia content associated with the artist, commented on the artist ormedia content associated with the artist, reviewed the artist or mediacontent associated with the artist, or rated the artist or media contentassociated with the artist.
 4. The computer-implemented method of claim2, wherein the external system is a social networking system, andwherein the level of interaction with the artist on the external systemincludes at least one of a number of posts regarding the artist on thesocial networking system, a number of users following the artist on thesocial networking system, or a number of endorsements of the artist onthe social networking system.
 5. The computer-implemented method ofclaim 1, wherein the recommendation of the at least one artist isincluded within at least one of a display page output by the musicdistribution system to a computing device associated with the at leastone user or an electronic message transmitted to the computing deviceassociated with the at least one user by the music distribution system.6. A system for identifying media item creators predicted to becomepopular, the system comprising: a data store configured to store userinteraction information reflective of interaction of users with mediaitems associated with a media information system; and a computer systemcomprising one or more computing devices, said computer systemconfigured with specific computer-executable instructions to at least:identify one or more popular media item creators based at least in parton the user interaction information, wherein each of the one or morepopular media item creators satisfies a set of popularity criteria;determine a set of users of the media information system that interactedwith content related to individual media item creators of the one ormore popular media item creators prior to popularity of the respectivepopular media item creators, wherein the set of users are determinedbased at least in part on the user interaction information; assignscores to individual users of the set of users, wherein a score of anindividual user is based at least in part on a length of time between i)an initial interaction of the individual user with content related tothe respective popular media item creators and ii) a point in time atwhich the respective popular media item creators satisfied the set ofpopularity criteria; process user interaction information associatedwith the determined set of users to identify a set of non-popular mediaitem creators on the media information system interacted with by the setof users; rank the set of non-popular media item creators based at leastin part on a level of interaction of the set of users with content ofthe non-popular media item creators and the scores of individual usersof the set of users; generate a recommendation of at least one mediaitem creator of the ranked set of non-popular media item creators to atleast one user of the media information system based at least in part ona rank of the at least one media item creator; and transmit therecommendation of the at least one media item creator to a computingdevice associated with the at least one user.
 7. The system of claim 6,wherein the media item creator is at least one of a band, an artist, anauthor, an editor, an engineer, a technician, or a producer of mediaitems.
 8. The system of claim 6, wherein the computing system is furtherconfigured to score the determined set of users based at least in parton one or more of a timing of each of the set of users' interaction withrespective popular media item creators of the set of popular media itemcreators or a number of interactions of each of the set of users withrespective popular media item creators of the set of popular media itemcreators.
 9. The system of claim 6, wherein the recommendation istransmitted via at least one of a display page or electronic messagetransmitted by the one or more computing devices to the computing deviceof the at least one user.
 10. The system of claim 6, wherein the one ormore popular media item creators are associated with a specificgeographical area.
 11. Non-transitory computer storage having storedthereon executable instructions that direct a computer system comprisingone or more computing devices to perform a process that comprises:determining, based on artist popularity data and recorded behaviors ofusers, a plurality of early adopter users that have previously listenedto music of popular musical artists before such popular musical artistsbecame popular; assigning scores to individual early adopter users ofthe plurality of early adopter users, wherein a score of an individualearly adopter user is based at least in part on a length of time betweeni) an initial interaction of the individual early adopter user withindividual popular musical artists and ii) a point in time at which theindividual popular musical artists became popular; identifying a set ofmusical artists that, based on recorded behaviors of users, have not yetreached a selected popularity level; ranking the set of musical artistsbased at least in part on scores of the individual early adopter usersand how frequently the early adopter users listen to music of the set ofmusical artists; generating, based at least partly on the scores of theindividual early adopter users and a measure of how frequently the earlyadopter users listen to music of the musical artists, scores forindividual musical artists of the set of musical artists that representsa likelihood that the respective individual musical artists will atleast reach the selected popularity level; generating, from at least thescores of the individual musical artists, a recommendation of at leastone musical artist; and transmitting the recommendation to a computingdevice of a user.
 12. The non-transitory computer storage of claim 11,wherein the score assigned to individual early adopter users of theplurality of early adopter users is further based at least in part ondemographic information associated with the individual early adopterusers of the plurality of early adopter users.
 13. The non-transitorycomputer storage of claim 11, wherein the process further comprisesproviding a reward to at least one of the plurality of early adopterusers.